External seminars

Understanding the accessible genome from single cells

2018-02-20

Postdoc Researcher Xingqi Chen,

Stanford University, California

Wednesday, February 28,
15:15 BMC C2:305

Eukaryotic genomes are extensively compacted in chromatin, except for active regulatory elements whose access control gene activity. These accessible elements comprise approximately 1% of the genome in any given cell types and include enhancers, promoters, and other regulatory sequences critical in development and disease. The spatial organization of DNA sequences in the nucleus and nuclear architecture are tightly linked to gene expression, replication and DNA repair. Despite recent advances, current epigenomic methods extract regulatory DNA outside of the native context of the nucleus and reconstruct regulation on an imaginary linear genome, divorced from the intricate spatio-temporal organization evident in movies of living cells. Current technologies to explore the heterogeneity of cells mainly focus on improvement of the throughput, and largely ignore the directly link between cell identity and chromatin structure profile in the individual cells. Usually the cells are randomly sorted or distributed for the downstream process, and the cell identity was decoded from the next generation sequencing by modern computation tools. Thus, we could only interpret the cell behavior from the in silico tools, and miss the direct observation of the cell behavior. To completely characterize the cellular heterogeneity, it is essential to directly link the cell behavior with chromatin profile. The flow cytometry based high specific antibodies staining with index sorting are widely used to describe cell behaviors and cell types. To this end, We introduced two single cell technologies: ATAC-see (Assay of Transposase-Accessible Chromatin with visualization), a transposase-mediated imaging technology that enables direct imaging of the accessible genome in situ, cell sorting, and deep sequencing to reveal the identity of the imaged elements1, and MI-ATAC (Multi-Index single cell ATAC-sequencing), where we not only index the ATAC-seq profile for individual cells, but also index the protein expression level with high throughput index FACS sorting.